专著：

1.      Song Guo, Zhihao Qu, Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design, Cambridge University Press, 2022. (ISBN: 9781108832373)

期刊论文 (* indicates that I am the corresponding author)：

1.      Shihong Hu, Zhihao Qu*, Bin Tang, Baoliu Ye, Guanghui Li, and Weisong Shi, “Joint Service Request Scheduling and Container Retention in Serverless Edge Computing for Vehicle Infrastructure Collaboration,” IEEE Transactions on Mobile Computing, 2023, Preprint. (CCF A类期刊)

2.      Yue Zeng, Zhihao Qu*, Song Guo, Bin Tang, Baoliu Ye, Jing Li, and Jie Zhang, “RuleDRL Reliability-Aware SFC Provisioning with Bounded Approximations in Dynamic Environments,” IEEE Transactions on Services Computing, vol. 16, no. 5, pp. 3651-3664, 2023. (CCF A类期刊)

3.      Yue Zeng, Zhihao Qu*, Song Guo, Baoliu Ye, Jie Zhang, Jing Li, and Bin Tang, “SafeDRL Dynamic Microservice Provisioning with Reliability and Latency Guarantees in Edge Environments,”, IEEE Transactions on Computers, vol. 73, no. 1, pp. 235-248, 2023. (CCF A类期刊)

4.      Feijie Wu, Song Guo, Haozhao Wang, Haobo Zhang, Zhihao Qu*, Jie Zhang, and Ziming Liu, “From Deterioration to Acceleration-A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization,” IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 5, pp. 1548-1559, 2023. (CCF A类期刊)

5.      Yue Zeng, Baoliu Ye, Bin Tang, Sanglu Lu, Feng Xu, Song Guo, Zhihao Qu, “Mobility-aware Proactive Flow Setup in Software-Defined Mobile Edge Networks,” IEEE Transactions on Communications, vol.71, no.3, 2023.

6.      Lingyun Cui, Zhihao Qu*, Guomin Zhang, Bin Tang, Baoliu Ye“A Bidirectional DNN Partition Mechanism for Efficient Pipeline Parallel Training in Cloud,” Journal of Cloud Computing, vol. 12, no.22, 2023.

7.      Bin Fan, Bin Tang, Zhihao Qu, Baoliu Ye, “Network Coding Approaches for Distributed Computation over Lossy Wireless Networks,” Entropy, vol. 25, no. 3, 2023.

8.      Lin Qian, Zhihao Qu*, Miao Cai, Baoliu Ye, Xiaoliang Wang, Jianyu Wu, Weiguo Duan, Ming Zhao, Qiang Lin, “FastCache: A Write-optimized Edge Storage System via Concurrent Merging Cache for IoT applications,” Journal of Systems Architecture, vol. 131, no.3, 2022.

9.      Zhihao Qu, Song Guo, Haozhao Wang, Baoliu Ye, Yi Wang, Albert Y. Zomoya, and Bin Tang, “Partial Synchronization to Accelerate Federated Learning over Relay-Assisted Edge Networks,” IEEE Transactions on Mobile Computing, vol.21, no.12, 2022. (CCF A类期刊)

10.  Jie Zhang, Song Guo, Zhihao Qu, Deze Zeng, Haozhao Wang, Qifeng Liu, and Albert Zomaya, “Adaptive Vertical Federated Learning on Unbalanced Features,” IEEE Transactions on Parallel and Distributed Systems, 2022. (CCF A类期刊)

11.  Jie Zhang, Song Guo, Zhihao Qu*, Deze Zeng, Yufeng Zhan, Qifeng Liu, and Rajendra A. Akerkar “Adaptive Federated Learning on Non-IID Data with Resource Constraint,” IEEE Transactions on Computers, vol.71, no. 7, pp. 1655-1667, 2022. (CCF A类期刊)

12.  Haozhao Wang, Zhihao Qu, Song Guo, Ningqi Wang, Ruixuan Li, and Weihua Zhuang, “LOSP: Overlap Synchronization Parallel with Local Compensation for Fast Distributed Training,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2541-2557, 2021. (CCF A类期刊)

13.  Haozhao Wang, Song Guo, Zhihao Qu, Ruixuan Li, and Ziming Liu, “Error-Compensated Sparsification for Communication-Efficient Decentralized Training in Edge Environment,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 1, pp. 14-25, Jan. 2022. (CCF A类期刊)

14.  Qihua Zhou, Song Guo, Zhihao Qu, Peng Li, Li Li, Minyi Guo, and Kun Wang, “Petrel: Heterogeneity-aware Distributed Deep Learning via Hybrid Synchronization,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 5, pp. 1030-1043, 2021. (CCF A类期刊)

15.  Haozhao Wang, Song Guo, Bin Tang, Ruixuan Li, Chengjie Li, Yutong Yang, Zhihao Qu, “Heterogeneity-aware Gradient Coding for Tolerating and Leveraging Stragglers,” IEEE Transactions on Computers, 2021, Preprint. (CCF A类期刊)

16.  Jie Zhang, Zhihao Qu*, Chenxi Chen, Haozhao Wang, Yufeng Zhan, Baoliu Ye and Song Guo, “Edge Learning: The Enabling Technology for Distributed Big Data Analytics in the Edge,” ACM Computing Surveys, vol. 54, no. 7, pp. 1-36, 2021.

17.  Haozhao Wang, Zhihao Qu*, Qihua Zhou, Haobo Zhang, Boyuan Luo, Song Guo, and Ruixuan Li, “A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT,” Internet of Things Journal, vol. 9, no. 2, pp. 939-963, 2022.

18.  Qihua Zhou, Zhihao Qu*, Song Guo, Boyuan Luo, Jingcan Guo, Zhenda Xu, Rajendra Akerkar, “On-device Learning Systems for Edge Intelligence: A Software and Hardware Synergy Perspective,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 11916-11934, 2021.

19.  Yufeng Zhan, Peng Li, Song Guo, Zhihao Qu, Incentive Mechanism Design for Federated Learning: Challenges and Opportunities, IEEE Networks, vol. 35, no. 4, pp. 310-317, 2021.

20.  Zhihao Qu, Baoliu Ye, Bin Tang, Song Guo, Sanglu Lu, and Weihua Zhuang, “Cooperative Caching for Multiple Bitrate Videos in Small Cell Edges,” IEEE Transactions on Mobile Computing, vol. 19, no. 2, pp. 288-299, 2020. (CCF A类期刊)

21.  Yue Zeng, Baoliu Ye, Bin Tang, Songtao Guo, Zhihao Qu, “Scheduling Coflows of Multi-Stage Jobs under Network Resource Constraints,” Computer Networks, vol. 184, 2020.

22.  Tao Huang, Bin Tang, Baoliu Ye, Zhihao Qu, and Sanglu Lu, “Rateless802.11: Extending WiFi Applicability in Extremely Poor Channels”, Computer Networks, vol. 179, no. 9, 2021.

23.  Yufeng Zhan, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo, “A Learning-based Incentive Mechanism for Federated Learning,” IEEE Internet of Thing Journal, vol. 7, no. 7, 2020.

24.  Haozhao Wang, Zhihao Qu, Song Guo, Xin Gao, Ruixuan Li, Baoliu Ye, “Intermittent Pulling with Local Compensation for Communication-Efficient Distributed Learning,” IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 2, pp. 779-791, 2022.

会议论文(* indicates that I am the corresponding author)：

1.        Yuepeng Li, Lin Gu, Zhihao Qu, Lifeng Tian, Deze Zeng, “On Efficient Zygote Container Planning and Task Scheduling for Edge Native Application Acceleration”, in Proceedings of  International Conference on Computer Communications (INFOCOM) 2024. (CCF A类会议)

2.        Miao Cai, Junren Shen, Yifan Yuan, Zhihao Qu, Baoliu Ye, “BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System ,” in Proceedings of the 50th International Conference on Very Large Databases (VLDB) Volume 17, 2024. (CCF A类会议)

3.        Feijie Wu, Song Guo, Zhihao Qu*, Shiqi He, Ziming Liu, and Jing Gao, “Anchor Sampling for Federated Learning with Partial Client Participation”, in Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR 202:37379-37416, 2023.(CCF A类会议)

4.        Siyuan Zhou, Xiaofan Yu, Bin Tang, Zhihao Qu, “Handover Analysis with Spatially Correlated Blockage Model,” in Proceedings of the 19th International Conference on Mobility, Sensing and Networking (MSN), 2023.

5.        Shihong Hu, Zhihao Qu*, Bin Tang, and Baoliu Ye, “Joint Service Placement and Container Retention for Serverless-Based Vehicular Edge Computing,” in Proceedings of the 28th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2023.

6.        Zaipeng Xie, Jianan Zhang, Yida Zhang, Chenghong Xu, Peng Chen, Zhihao Qu, and WenZhan Song. “An Efficient Fault Tolerance Strategy for Multi-task MapReduce Models Using Coded Distributed Computing.” In Proceedings of the 23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), 20-22 October 2023, Tianjin, China.

7.        Shunpeng Hua, Baoliu Ye, Yue Zeng, Zhihao Qu, and Bin Tang, “Joint Controller Placement and Flow Assignment in Software-Defined Edge Networks,” In Proceedings of the 23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), 20-22 October 2023, Tianjin, China.

8.        Wenzhong Wang, Zaipeng Xie, Bingzhe Yu, Zhihao Qu, Yufeng Zhang, and Hongli Cao. “Federated Learning with Common Representation Learning Criterion and Personalized Predictor,” In Proceedings of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). October 1-4, 2023, Hyatt Maui, Hawaii, USA.

9.        Zaipeng Xie, Junchen Jiang, Ruifeng Chen, Zhihao Qu, and Hanxiang Liu. “FedDGIC: Reliable and Efficient Asynchronous Federated Learning with Gradient Compensation.” in Proceedings of the 28th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2022.

10.    Qihua Zhou, Song Guo, Yi Liu, Jie Zhang, Jiewei Zhang, Tao Guo, Zhenda Xu, Xun Liu, Zhihao Qu, “Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning, ” in Proc. of 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. (CCF A类会议)

11.    Zaipeng Xie, Yao Liu, Zhihao Qu, Bin Tang, Weiyi Zhao, “FedALP: An Adaptive Layer-Based Approach for Improved Personalized Federated Learning,” in Proc. of the 17th International Conference on Wireless Algorithms, Systems, and Applications (WASA), 2022.

12.    Weiyong Yang, Peng Gao, Hao Huang, Xingshen Wei, Haotian Zhang, Zhihao Qu, “Advanced Persistent Threat Detection in Smart Grid Clouds Using Spatiotemporal Context-Aware Graph Embedding,” in Proc. of IEEE Global Communications Conference (GLOBECOM), 2022.

13.    Siqi Huang, Deze Zeng, Zhihao Qu, “Toward Performance Efficient UAV Task Scheduling in Cloud Native Edge,” in Proc. of IEEE Global Communications Conference (GLOBECOM), 2022.

14.    Wenxuan Zhou, Zhihao Qu*, Yanchao Zhao, Bin Tang, Baoliu Ye, “An Efficient Split Learning Framework for Recurrent Neural Network in Mobile Edge Environment,” in Proc. of International Conference on Research in Adaptive and Convergent Systems (RACS), 2022.

15.    Feijie Wu, Shiqi He, Song Guo, Zhihao Qu*, Haozhao Wang, Weihua Zhuang, Jie Zhang, “Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression,” in Proc. of Design Automation Conference (DAC), 2022. (CCF A类会议)

16.    Qihua Zhou, Song Guo, Zhihao Qu, Jingcai Guo, Zhenda Xu, Jiewei Zhang, Tao Guo, Boyuan Luo, and Jingren Zhou, “Octo: INT8 Training with Loss-aware Compensation and Backward Quantization for Tiny On-device Learning” in Proc. of USENIX Annual Technical Conference (USENIX ATC), 2021. (CCF A类会议)

17.    Ninghui Jia, Zhihao Qu* and Baoliu Ye, “Communication-efficient Federated Learning via Quantized Clipped SGD,” in Proc. of the 16th International Conference on Wireless Algorithms, Systems, and Applications (WASA), 2021.

18.    Lin Qian, Baoliu Ye, Xiaoliang Wang, Zhihao Qu, Weiguo Duan, and Ming Zhao, “FastCache: A Client-Side Cache With Variable-Position Merging Schema in Network Storage System,” in Proc. of International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), 2021.

19.    Tao Huang, Baoliu Ye, Zhihao Qu, Bin Tang, Lei Xie, and Sanglu Lu, “Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks,” in Proc. of International Conference on Computer Communications (INFOCOM), 2020. (CCF A类会议)

20.    Lei Zhang, Zhihao Qu*, Baoliu Ye and Bin Tang, “Joint Service Placement and Computation Offloading in Mobile Edge Computing: An Auction-based Approach,” in Proc. of the 26th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2020.

21.    Yamei Dong, Bin Tang, Baoliu Ye, Zhihao Qu and Sanglu Lu, “Intermediate Value Size Aware Coded MapReduce,” in Proc. of the 26th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2020.

22.    Jie Zhang, Song Guo, Deze Zeng, Zhihao Qu, “Multi-path Routing Oriented Flow Statistics Collection in Software Defined Networks,” in Proc. of the 25th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2019.

23.    Zhihao Qu, Baoliu Ye, Bin Tang, Sanglu Lu, and Song Guo, “Energy-aware Cost-effective Cooperative Mobile Streaming on Smartphone Over Hybrid Wireless Networks,” in Proc. of the 44th International Conference on Parallel Processing (ICPP), 2015.

24.    Zhihao Qu, Baoliu Ye, Bin Tang, and Sanglu Lu, “Fast Cooperative Content Distribution over Hybrid Wireless Networks,” in Proc. of IEEE Global Telecommunication Conference (GLOBECOM), 2015.

国内刊物：

1.      郭得科，曾德泽，徐子川，屈志昊，彭晓晖，周知，张星洲，唐国明，陈旭，叶保留，边缘计算得研究进展与发展趋势，2020-2021中国计算机科学技术发展报告。

2.      屈志昊，叶保留，陈贵海，唐斌，郭成昊，面向边缘计算的资源优化技术研究进展，大数据，专题17，2019。

主持的科研项目：

1.      国家自然科学基金青年基金，面向无梯度联邦学习的训练加速技术，No. 62102131，2022.1~2024.12，主持。

2.      江苏省自然科学基金青年基金项目，面向边缘智能的联邦学习性能优化技术研究，No. BK20210361， 2021.7~2024.6，主持。

3.      南瑞集团科技项目，边缘侧低可信环境下终端设备安全信任度度量技术研究，2021.07~2021.12，主持。

4.      国家电网科技项目，基于零信任的电力监控系统网络安全准入关键技术研究及装备研制，2022.01~2023.12，河海大学课题负责人。

5.      国家电网科技项目，国网江苏信通公司电力物联网软件定义多实体模型及互操作研究服务，2023.07~2024.12，主持